code: def test_fabric_module_forward_conversion(
      precision, input_type, expected_type, accelerator, device_str
  ):
      """Test that the FabricModule performs autocasting on the input tensors and during forward()."""
      fabric = Fabric(
          precision=precision, accelerator=accelerator, devices=1
      )
      device = torch.device(device_str)
  
      def check_autocast(forward_input):
          assert precision != 16 or torch.is_autocast_enabled()
          return forward_input
  
      module = Mock(
          wraps=torch.nn.Identity(), side_effect=check_autocast
      )
      fabric_module = _FabricModule(module, fabric._precision).to(
          device
      )
      out = fabric_module(
          torch.tensor([1, 2, 3], dtype=input_type, device=device)
      )
      assert module.call_args[0][0].dtype == expected_type
      assert (
          out.dtype == input_type
          or out.dtype == torch.get_default_dtype()
      )
  
imports: from unittest import mock
  from unittest.mock import Mock, call
  import pytest
  import torch
  from lightning.fabric.fabric import Fabric
  from lightning.fabric.plugins import Precision
  from lightning.fabric.utilities.device_dtype_mixin import _DeviceDtypeModuleMixin
  from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_1
  from lightning.fabric.wrappers import _FabricDataLoader, _FabricModule, _FabricOptimizer, _unwrap_objects, is_wrapped
  from torch.utils.data import BatchSampler, DistributedSampler
  from torch.utils.data.dataloader import DataLoader
  from tests_fabric.helpers.runif import RunIf
label_desc: Use image node_id 6 for calling a global function with example usage: test_fabric_module_forward_conversion(precision, input_type, expected_type, accelerator, device_str) and returns: forward_input